Auxiliary Particle Implementation of Probability Hypothesis Density Filter
نویسندگان
چکیده
منابع مشابه
Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters
The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...
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Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior distribution, which he called the Probability Hypothesis Density (PHD) filter. While this reduces the dimensionality of the problem, the PHD filter s...
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Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularl...
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ژورنال
عنوان ژورنال: IEEE Transactions on Aerospace and Electronic Systems
سال: 2010
ISSN: 0018-9251
DOI: 10.1109/taes.2010.5545199